Harnessing the power of text mining for the detection of abusive content in social media

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The issues of cyberbullying and online harassment have gained considerable coverage in the last number of years. Social media providers need to be able to detect abusive content both accurately and efficiently in order to protect their users. Our aim is to investigate the application of core text mining techniques for the automatic detection of abusive content across a range of social media sources include blogs, forums, media-sharing, Q&A and chat—using datasets from Twitter, YouTube, MySpace, Kongregate, Formspring and Slashdot. Using supervised machine learning, we compare alternative text representations and dimension reduction approaches, including feature selection and feature enhancement, demonstrating the impact of these techniques on detection accuracies. In addition, we investigate the need for sampling on imbalanced datasets. Our conclusions are: (1) Dataset balancing boosts accuracies significantly for social media abusive content detection; (2) Feature reduction, important for large feature sets that are typical of social media datasets, improves efficiency whilst maintaining detection accuracies; (3) The use of generic structural features common across all our datasets proved to be of limited use in the automatic detection of abusive content. Our findings can support practitioners in selecting appropriate text mining strategies in this area.

Original languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems - Contributions Presented at the 16th UK Workshop on Computational Intelligence, 2016
EditorsAlexander Gegov, Chrisina Jayne, Qiang Shen, Plamen Angelov
PublisherSpringer Verlag
Pages187-205
Number of pages19
ISBN (Print)9783319465616
DOIs
Publication statusPublished - 2017
Event16th UK Workshop on Computational Intelligence, UKCI 2016 - Lancaster, United Kingdom
Duration: 7 Sep 20169 Sep 2016

Publication series

NameAdvances in Intelligent Systems and Computing
Volume513
ISSN (Print)2194-5357

Conference

Conference16th UK Workshop on Computational Intelligence, UKCI 2016
Country/TerritoryUnited Kingdom
CityLancaster
Period7/09/169/09/16

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